from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__ = None )      ->str:




  '''simple docstring'''

  if version.parse(hfh.__version__ ).release < version.parse("0.11.0" ).release:
    # old versions of hfh don't url-encode the file path
    _UpperCamelCase           =    quote(a__ )
  return hfh.hf_hub_url(a__    ,     a__    ,     repo_type="dataset"    ,     revision=a__ )





import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 					_UpperCAmelCase			(     lowerCAmelCase,			unittest.TestCase  ):



 '''simple docstring'''




 __A			       =					DDIMPipeline
 __A			       =					UNCONDITIONAL_IMAGE_GENERATION_PARAMS
 __A			       =					PipelineTesterMixin.required_optional_params - {
     '''num_images_per_prompt''',
     '''latents''',
     '''callback''',
     '''callback_steps''',
 }
 __A			       =					UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
 __A			       =					False







 def 		__UpperCAmelCase				(		self				:   int)						->	List[str]:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase           =    UNetaDModel(
       block_out_channels=(32, 64)							, layers_per_block=2							, sample_size=32							, in_channels=3							, out_channels=3							, down_block_types=("DownBlock2D", "AttnDownBlock2D")							, up_block_types=("AttnUpBlock2D", "UpBlock2D")							, )
   _UpperCamelCase           =    DDIMScheduler()
   _UpperCamelCase           =    {"unet": unet, "scheduler": scheduler}
   return components







 def 		__UpperCAmelCase				(		self				:   int							, lowercase_				:   List[str]							, lowercase_				:   Optional[Any]=0)						->	Optional[Any]:


   """simple docstring"""







   if str(lowercase_).startswith("mps"):
     _UpperCamelCase           =    torch.manual_seed(lowercase_)
   else:
     _UpperCamelCase           =    torch.Generator(device=lowercase_).manual_seed(lowercase_)
   _UpperCamelCase           =    {
       "batch_size": 1,
       "generator": generator,
       "num_inference_steps": 2,
       "output_type": "numpy",
   }
   return inputs







 def 		__UpperCAmelCase				(		self				:   Dict)						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    "cpu"

   _UpperCamelCase           =    self.get_dummy_components()
   _UpperCamelCase           =    self.pipeline_class(**lowercase_)
   pipe.to(lowercase_)
   pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    self.get_dummy_inputs(lowercase_)
   _UpperCamelCase           =    pipe(**lowercase_).images
   _UpperCamelCase           =    image[0, -3:, -3:, -1]

   self.assertEqual(image.shape							, (1, 32, 32, 3))
   _UpperCamelCase           =    np.array(
       [1.0_0_0e0_0, 5.7_1_7e-0_1, 4.7_1_7e-0_1, 1.0_0_0e0_0, 0.0_0_0e0_0, 1.0_0_0e0_0, 3.0_0_0e-0_4, 0.0_0_0e0_0, 9.0_0_0e-0_4])
   _UpperCamelCase           =    np.abs(image_slice.flatten() - expected_slice).max()
   self.assertLessEqual(lowercase_							, 1e-3)







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Union[str, Any]:


   """simple docstring"""







   super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)







 def 		__UpperCAmelCase				(		self				:   List[Any])						->	Union[str, Any]:


   """simple docstring"""







   super().test_save_load_local(expected_max_difference=3e-3)







 def 		__UpperCAmelCase				(		self				:   int)						->	str:


   """simple docstring"""







   super().test_save_load_optional_components(expected_max_difference=3e-3)







 def 		__UpperCAmelCase				(		self				:   List[Any])						->	Dict:


   """simple docstring"""







   super().test_inference_batch_single_identical(expected_max_diff=3e-3)




@slow
@require_torch_gpu
class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   List[Any])						->	str:


   """simple docstring"""







   _UpperCamelCase           =    "google/ddpm-cifar10-32"

   _UpperCamelCase           =    UNetaDModel.from_pretrained(lowercase_)
   _UpperCamelCase           =    DDIMScheduler()

   _UpperCamelCase           =    DDIMPipeline(unet=lowercase_							, scheduler=lowercase_)
   ddim.to(lowercase_)
   ddim.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    torch.manual_seed(0)
   _UpperCamelCase           =    ddim(generator=lowercase_							, eta=0.0							, output_type="numpy").images

   _UpperCamelCase           =    image[0, -3:, -3:, -1]

   assert image.shape == (1, 32, 32, 3)
   _UpperCamelCase           =    np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    "google/ddpm-ema-bedroom-256"

   _UpperCamelCase           =    UNetaDModel.from_pretrained(lowercase_)
   _UpperCamelCase           =    DDIMScheduler.from_pretrained(lowercase_)

   _UpperCamelCase           =    DDIMPipeline(unet=lowercase_							, scheduler=lowercase_)
   ddpm.to(lowercase_)
   ddpm.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    torch.manual_seed(0)
   _UpperCamelCase           =    ddpm(generator=lowercase_							, output_type="numpy").images

   _UpperCamelCase           =    image[0, -3:, -3:, -1]

   assert image.shape == (1, 256, 256, 3)
   _UpperCamelCase           =    np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2




